This position is critical in driving innovations in semantic search, reranking methodologies, and evaluating search performance to deliver optimal, intelligent solutions. With expertise spanning multiple advanced domains, the Data Scientist will contribute significantly to building and refining systems that improve the relevance, accuracy, and efficiency of information access.

Roles and Responsibilities:

Design and develop models and algorithms for Information Retrieval and semantic search.
Implement and refine reranking strategies to optimize search results.
Evaluate and enhance search performance using qualitative and quantitative methods.
Apply Machine Learning and Artificial Intelligence techniques to solve complex data challenges.
Integrate Generative AI capabilities into search and retrieval workflows.
Collaborate with cross-functional teams, including data engineers, product managers, and UX designers, to align solutions with business goals.
Conduct experiments and A/B tests to measure the effectiveness of implemented algorithms.
Identify gaps and opportunities for improvement in search and retrieval systems.
Stay current with the latest research and advancements in AI, ML, and semantic technologies.
Contribute to technical documentation, reports, and best practice guidelines.

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
5+ years of experience as a Data Scientist or in a similar role.
Strong background in Information Retrieval, Machine Learning, and Artificial Intelligence.
Hands-on experience with Generative AI models and semantic search methodologies.
Proven expertise in reranking algorithms and search performance evaluation.
Solid understanding of data structures, algorithms, and statistical methods.
Experience with large-scale data processing and distributed systems.
Excellent analytical, problem-solving, and communication skills.
Ability to work collaboratively in a fast-paced, research-driven environment.

Tools and Technologies:

Proficiency in programming languages such as Python, Java, or Scala.
Experience with Machine Learning frameworks (TensorFlow, PyTorch, Scikit-learn).
Familiarity with search engines and libraries (Elasticsearch, Solr, Vespa, or equivalent).
Knowledge of NLP libraries and tools (SpaCy, Hugging Face Transformers).
Competency in using data processing tools (Spark, Hadoop).
Experience with cloud platforms (AWS, Azure, or GCP) for ML/AI deployments.
Strong knowledge of experiment tracking and evaluation tools (MLflow, Weights & Biases).